{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp data.image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imaging Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Main functions used to transform time series into TSImage tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from tsai.imports import *\n",
    "from matplotlib.backends.backend_agg import FigureCanvasAgg\n",
    "from pyts.image.gaf import GramianAngularField\n",
    "from pyts.image import MarkovTransitionField, RecurrencePlot\n",
    "from pyts.multivariate.image import JointRecurrencePlot\n",
    "from fastai.vision.augment import *\n",
    "from fastai.vision.core import *\n",
    "from tsai.utils import *\n",
    "from tsai.data.external import *\n",
    "from tsai.data.core import *\n",
    "from tsai.data.preprocessing import *\n",
    "from tsai.data.transforms import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsid = 'NATOPS'\n",
    "X, y, splits = get_UCR_data(dsid, return_split=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "class TSImage(TensorImage):\n",
    "    def __getitem__(self, idx):\n",
    "        res = super().__getitem__(idx)\n",
    "        return res.as_subclass(type(self))\n",
    "\n",
    "    @property\n",
    "    def vars(self):\n",
    "        return self.shape[-3]\n",
    "\n",
    "    @property\n",
    "    def len(self): return self.shape[-2:]\n",
    "\n",
    "    def __repr__(self):\n",
    "        if self.ndim == 0: return f'{self.data}'\n",
    "        else: return f'TSImage(shape:{self.shape})'\n",
    "\n",
    "    def show(self, **kwargs):\n",
    "        if self.ndim < 3:\n",
    "            while True:\n",
    "                self = self[None]\n",
    "                if self.ndim == 3: break\n",
    "        elif self.ndim > 3:\n",
    "            while True:\n",
    "                self = self[0]\n",
    "                if self.ndim == 3: break\n",
    "        if self[:3].shape[0] == 3 and kwargs == {}:\n",
    "            display(to_image(self[:3]))\n",
    "            return\n",
    "        else:\n",
    "            TensorImage(self[:3]).show(**kwargs)\n",
    "            return\n",
    "\n",
    "\n",
    "class ToTSImage(Transform):\n",
    "    order = 99\n",
    "    \"Transforms an object into TSImage\"\n",
    "    def encodes(self, o: TensorImage): return TSImage(o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "class TSToPlot(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by creating a matplotlib plot.\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size:Optional[int]=224, dpi:int=default_dpi(), lw=1, **kwargs):\n",
    "        self.size, self.dpi, self.lw, self.kwargs = size, dpi, lw, kwargs\n",
    "        self.fig = get_plot_fig(size, dpi=dpi)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        device = o.device\n",
    "        if o.data.device.type != 'cpu': o = o.cpu()\n",
    "        if o.ndim == 2: o = o[None]\n",
    "        seq_len = o.shape[-1]\n",
    "        fig = self.fig\n",
    "        if self.size is None: fig.set_size_inches(seq_len / self.dpi, seq_len / self.dpi)\n",
    "        canvas = FigureCanvasAgg(fig)\n",
    "        ax = fig.get_axes()[0]\n",
    "        ax.set_xlim(0, seq_len - 1)\n",
    "        output = []\n",
    "        for oi in o:\n",
    "            start = time.time()\n",
    "            ax.plot(oi.T.numpy(), lw=self.lw, **self.kwargs)\n",
    "            canvas.draw()\n",
    "            buf = np.asarray(canvas.buffer_rgba())[..., :3]\n",
    "            output.append(tensor(buf / 255).permute(2,0,1)[None])\n",
    "        return TSImage(torch.cat(output)).to(device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 3, 224, 224])\n"
     ]
    },
    {
     "data": {
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",
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = TSToPlot()(TSTensor(X[:2]), split_idx=0)\n",
    "print(out.shape)\n",
    "out[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "class TSToMat(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by creating a matplotlib matrix.\n",
    "    Input data must be normalized with a range(-1, 1)\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, dpi=default_dpi(), cmap=None, **kwargs):\n",
    "        self.size, self.dpi, self.cmap, self.kwargs = size, dpi, cmap, kwargs\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        device = o.device\n",
    "        if o.data.device.type != 'cpu': o = o.cpu()\n",
    "        if o.ndim == 2: o = o[None]\n",
    "        nvars, seq_len = o.shape[-2:]\n",
    "        aspect = seq_len / nvars\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        fig = get_plot_fig(self.size, dpi=self.dpi)\n",
    "        ax = fig.get_axes()[0]\n",
    "        ax.set_xlim(0, seq_len-1)\n",
    "        canvas = FigureCanvasAgg(fig)\n",
    "        output = []\n",
    "        for oi in o:\n",
    "            if output == []: im = ax.imshow(oi, aspect=aspect, vmin=-1, vmax=1, cmap=self.cmap, **self.kwargs)\n",
    "            else: im.set_data(oi)\n",
    "            canvas.draw()\n",
    "            buf = np.asarray(canvas.buffer_rgba())[..., :3]\n",
    "            canvas.flush_events()\n",
    "            output.append(tensor(buf / 255).permute(2,0,1)[None])\n",
    "        return TSImage(torch.cat(output)).to(device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 3, 224, 224])\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = TSToMat()(TSTensor(X[:2]), split_idx=0)\n",
    "print(out.shape)\n",
    "out[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 3, 224, 224])\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOAAAADgCAIAAACVT/22AAASUElEQVR4Ae2dy29V1xXGARvbGPMyGMyjvCEJaZrQvGia0qhSVKmDSu2gow467KB/UIeVOq+qDio1qqqkifJsQpo3CQQIhKcxbwx+QNe91+es317c65o85OX4u4P423uvvc+63/pykdY6e+/Fd+/eXWSfX/218d/wubvYO4gnur3f0BSaXVPF0Mqr3lx/3vGWU44N7Trizc2nHRva86k3x3scv/eoY0Nnh705tcRxQHs/846RdY4NjS3z5oltjg2d2uzNlw86fu8Hjr8zaOiCf5Wdnzs21H8DTWgDvU3YVFSr824Zi8UYGl/q8/puOzZ0p7n4v35msJxfWKkhBuaeAQl07mMgD2ZgQAKdgRwNzT0DEujcx0AezMCABDoDORqaewYk0LmPgTyYgQEJdAZyNDT3DEigcx8DeTADA4unE/UzpF1nmP2VhwZHi6lPv+7NA284NvTof715daXj05scG7qx3JutNG/d5lf75MG6e9GB1xwb6kaJYVNZLNh51C277jhG1rnROTLkQ7f6HBu6As9Z17hT/kBMIHc92VWsMIkhftnz6wuzw3u9+cYBx4ZGB4tm/kaT3pKg/E7LwwXGgAS6wAI+376uBDrfIrbA/JVAF1jA59vXlUDnW8QWmL8S6AIL+Hz7uhLofIvYAvNXAl1gAZ9vX1cCnW8RW2D+VpWkdRfbfHG+oF9s+UBVw6axwNNmla/XtRdbPh762NfihoFGL+pFYdsJmyzwDJ/11Qyd2ejNL7Y6NvQltnxcW1EMfYUG/Vlb0r7yiq/Hypb19oz7EOtPoeB0B/Wn4CpXWIx6WN8tXzmg8d6igyU67uXgN7IJlM0U/LGhSewO4hagsP2mZfZlo1ioX9AiBGpkY0ACzRYR+VMwIIEWdKiRjQEJNFtE5E/BgARa0KFGNgYk0GwRkT8FAxJoQYca2RiQQLNFRP4UDFSJ+n8+X3S3GkznMm/MfLJZLr/pc7smHRsa6/cmN2kc3+79hj7f6c2QJF8y5UPD5xy/dNCxIaaUmU8ujKyBfP6icsfGpUG33fOZY0O/+Ls3dxxz/Mu/OTbETHgxMG8bHzxcuD661ptXOxcselFTYH3HJjNvz3x+dymb1qznX7AZ+gV1zoUSMiCBJgyKXHIGJFDnQighAxJowqDIJWdAAnUuhBIyIIEmDIpccgYkUOdCKCEDEmjCoMglZ6BK1DOB7aPfKOIpS0++VSy9/x1v7kQm3HoHrvvQof2O//h7x4ZWX/Ymc/vWu6RMyNd2vJLBOlliOPhybdUAP/+HN1dcdcxzmqyX1zBcWeVmhvhyO5PSIY99fcBn3S7fZudegMtYnLdH2OSP9vkKLz3n2BDfwy8GsjaacdMvaNbwyK8mAxKohJCaAQk0dXjknAQqDaRmQAJNHR45J4FKA6kZkEBTh0fOSaDSQGoGJNDU4ZFzVSVpaKQNF3wpnzUPVjVsGusfbVb5el3PvejzeZrPv8stH+vof1k64v0YrKZcWuMrG+IBQyxf2dC+j9ySO1J4nJNZcNbwGZ9i6IeHvLn3sONw3/VGzOofc7OAuKdloqcYZHnsyO5iqHnU0XTPuWEfCveEsDRFbBNYD+MpS9SJmXXaKWRDVBEx41J7dmy7Qf2C1nwIZGRAAs0YFflUMyCB1lQIZGRAAs0YFflUMyCB1lQIZGRAAs0YFflUMyCB1lQIZGRAAs0YFflUM1Al6sMl2PV4W8AUqxkwyxqGmP3mUktwiL/18xx/prttaBny1byt4SLOCTIzPpfYhuge/eHKZjaCBa+stg7/hIS8D+RGSycK//pxhFbvbR+KhMNs4IabGSJ7zMbzQgUzYx2hmF9uv5lhhdbQK8/abP2CBgrVzMWABJorHvImMCCBBkLUzMWABJorHvImMCCBBkLUzMWABJorHvImMCCBBkLUzMWABJorHvImMFAl6jefbgyEFHd4U7qeGsxC1r02M8AVBq75CC9XsF6+qh0uMFh51WddWu04vMbPVH84Mok+9OJmX75pb+syrc2ctg0NnffnLp10zAOhrHc5jpGimQ2RsR4kyXvKXDrN/DFNxCGWHm72F4bcJnBuQzHEMgfNwjFSXJAPtbXIZCdcPLL84nEFFmvC2WDNPRHHdtgM/YIGRtXMxYAEmise8iYwIIEGQtTMxYAEmise8iYwIIEGQtTMxYAEmise8iYwIIEGQtTMxYAEmise8iYwIIEGQtTMxUD3tDu//st9+DVVypolmXA3BZu8ivny6uJxvPiC91SYEcshG875rN1HHRvicUx3uooh1l1uoO5yYagwGxz1Jlez3r6VPsSSFYsuZsEvGLY9rLrqK/DLTiz1fkMcYqmmMCrLM/x2ZsbaD4twNsTy2LYTvuTScceGWAO7XR78xMvM+dxQSuTOkGLpECaoKBT/pmf9wf7CKKylphhIwIAEmiAIcqEzAxJoZ240koABCTRBEORCZwYk0M7caCQBAxJogiDIhc4MSKCdudFIAgYk0ARBkAudGai2fLz9eGebe0ZCDpn3XYehe6ZOd4QENS/QZm7frFdd8TW4+FRVYmgNM0FN7JPvQVzNBnuQr157sbBmxptmhVH6Bjm/NuDucvuH9XI3zuigmxmaRAWE1ZnCyHLr2MvBA5jMLHBeTwxbX1r9z75sf/ULWpMkkJEBCTRjVORTzYAEWlMhkJEBCTRjVORTzYAEWlMhkJEBCTRjVORTzYAEWlMhkJEBCTRjVORTzUCVqN9wvu66b8DEOJO0thATs3wHO1xgwKG1I4UDPIOJN/vO8E5+8KFYDg36FlwN2fj9uKuYuwn48rmtsAbv5IfrB/DY4saI5eUtBXy5neUPTjfM26bDW/2k5dTmYh5vhuA5TcQ2gQsyLsVa5YUKYYh6CEMMDXF4UCs0ZxtnS+kXNFCoZi4GJNBc8ZA3gQEJNBCiZi4GJNBc8ZA3gQEJNBCiZi4GJNBc8ZA3gQEJNBCiZi4GJNBc8ZA3gQEJNBCiZi4Gqo0Tz79wH36FvD+P7JmhisCh68uLx93u82YY6sVOjH7UXbiBwSZPVl/EF7pPxBOF+nARhy3DmsfGM77u+rL8xnuteRCST2giPohXi9ggh/hQGwp1r3pNsmqdLHSx8GZD3EsztqxeYBGLT9Z7A6EJu0FIMrGvdQ/q5HYwbG/2G7PSL2igSs1cDEigueIhbwIDEmggRM1cDEigueIhbwIDEmggRM1cDEigueIhbwIDEmggRM1cDEigueIhbwIDVX771PcaAyE5zFsBmEplv83ikT3hAgPeCsA89vbjjcfVHx5+9PjbdXcD8OIE3tp9ZFdhxnz13c7/1/EKhFBuYFK6eRG0r//ZHsevPuP4/HrH8xeFYgHPpQqVCFqGQ5c6fv3ynm0KjIoiLpfqHMvSTi0xMCcMSKBzQrseOlsGJNDZMiW7OWFAAp0T2vXQ2TIggc6WKdnNCQMS6JzQrofOlgEJdLZMyW5OGJBA54R2PXS2DFRnM5X51NnO/sp2IQO845iv9KPXHBvadcSbZ4cdn9zq2BBzyHwvvTAqX1kPBzDxdf2NZ4t5qy97kxUB3v1gFt1TbsbX162XBx4xKU1sZnwfPpQ8WFbgaqwv2Aont7gPhx90bOgLMDayrhjK2WjeOa1f0JzBkVfTDEigkkJqBiTQ1OGRcxKoNJCaAQk0dXjknAQqDaRmQAJNHR45J4FKA6kZkEBTh0fOVZWknzSuPl7EMok1u3CvMisZl1Y3jOvPLRz0E0oULKjwdX/WQup12gLuQFhx1U3Cvg7uQGBFxybwuaz9cGUzu4jiCjexNFZo1jRaz+ZJRuM9rb7p/3IoVLNYOVtxzWfxWg/r5d0g3RNuZoixKBaHb2ZGlzilsQIKXQw0+82MxS2uZkN3UG8kJzbEDxekbMyGpTK6x4eaWcu9F39qUL+gpFY4HQMSaLqQyCEyIIGSDeF0DEig6UIih8iABEo2hNMxIIGmC4kcIgMSKNkQTseABJouJHKIDFSJ+jefZm8bzFRq2CzBbDMPP7JVpqqznwwzOX+ucdOyf9g8vt37DfGwft5McGR3Ycaj/0NymElpYuaTbS0eI/X9D4rFn3rTm0MXHAczH5jPiHd9hwOwrq/wL8YSTCCcUiH2yU3EoaColuWBxuYf/YIG2tTMxYAEmise8iYwIIEGQtTMxYAEmise8iYwIIEGQtTMxYAEmise8iYwIIEGQtTMxYAEmise8iYwUCXq8a50sPjGmruO+lJ7Dzs2tBtDPKfJhvjCOM9jOrS/WIFZX75db0Z895tmy8aKFQbwovu+j4uh5bhleRjHNoVUP++WCO+i82X78Fw+iW/ys8ZhNkyGXxvwSaNrHRtimePDhzsOBfcKuzSNZuj1C5omHnKkHQMSaDtW1JeGAQk0TSjkSDsGJNB2rKgvDQMSaJpQyJF2DEig7VhRXxoGJNA0oZAj7RiQQNuxor40DEigaUIhR9oxUFWSHiprJy1T1mB4Lk+ooPDt/xsoctgiZza2e+iMfb23i+H973hzvNfxxbKCwiqOGzURq0cc4sUg1s9dB7we3IY2n/J53BERij1dk242fM6xoS1YYXDUh8L5UGswxKOLbEIPaOFzWXwyswkcF3VhyB9k6MpKb5K9y2u83xCHwn3j1xHcW4gFdWIr8ACmpeDEhrjlpij4tatkHnrMZugX1EjQJy8DEmje2MgzY0AClQxSMyCBpg6PnJNApYHUDEigqcMj5yRQaSA1AxJo6vDIue5pCkLGuNXL7CkTwuGm6GW3nMfBS44NPfK+N/mI7ce939Bj73qTCXPr5eYQJqhPbfEphlgsYKLYhphG5oUKxXw7BKrLOz59wLGh9x/x5nuPOj66y/F3Bq264l9l2wnHhnjwVv+YD3E7ivUW2Xhu2bFY3PFZRCwDsV9pppINtdIxoH/i04VEDpEBCZRsCKdjQAJNFxI5RAYkULIhnI4BCTRdSOQQGZBAyYZwOgYk0HQhkUNkoHqjnjl5jn9LmClfe8TTb/hz9n7q2BBf9eeJQi8dLMx6x71ZvKpt3bP7bk++5SuwpmC9W076EKsSY33eb4i7CfjyuQ3Rki+ZM6dtZsx4s3BgQyxS8DqK8No8awevPmPz/BMuovaBrEhnM2WNjPxyBvRPvHMhlJABCTRhUOSSMyCBOhdCCRmQQBMGRS45AxKocyGUkAEJNGFQ5JIzIIE6F0IJGZBAEwZFLjkDVSXpibcbfTMUNviyPu++tlm8TJvXWfhTvgbiMUnc/sFrum35Phxd1D1RPI+Wk0t9iGUb6+XhR8d3uJmhS2u8yb0l3vv/ENnjhd57yrIZd6eEM6qKL8XaWLmn4i5+cT4pN65sOO9estjGIpxZ8Cirayt8iiHupeGDeJO5mbE5AcJtaMmUL9hNHI5walq98mxjhk8QEgP5GJBA88VEHoEBCRRkCOZjQALNFxN5BAYkUJAhmI8BCTRfTOQRGJBAQYZgPgYk0HwxkUdgoErUv7u/0VkmfYu9Ekw1h+OTmIwNQ7xygDn8kbXwwe5a2OTNcOjSmks+xIusXz/g/Q0E1/kgGxnAZdqcc305W8WxQTfLod/+2S33HHH841ccG2KavRiYVw3uq/loX+E6ozm2zIfCpRrMxrtRE/FUL5YeAnUtsT3V2AikX9BAoZq5GJBAc8VD3gQGJNBAiJq5GJBAc8VD3gQGJNBAiJq5GJBAc8VD3gQGJNBAiJq5GJBAc8VD3gQGqkQ939EOJt9U8/HmS/ut1XgQkvU8/KE/ZONpx4ZObPMmbzv+zxPeb4jvgb/1ZDHEg59YbgjnQ3E3AV/jt7WeedUXXH3ZcUhQr8cr6+Ga5xv9PotvkvMddbPg6U4T1f0WrZkc4o6Gs8O+siHSFWoZwbKYlrLRrL3oFzRlbORUxYAEWjGhvykZkEBThkVOVQxIoBUT+puSAQk0ZVjkVMWABFoxob8pGZBAU4ZFTlUMSKAVE/qbkgEJNGVY5FTFQFVJ2ni20cNqijV5yA5POOIeDzMbHWzM/ZY+v/uTL8wzkj550PsN0fMhVHRsaGTILWnWf9P7DZ1f702aWe8Dh33oi62OyYn1bvrSh1Zcd2xoM4Z4x/XWE4XZmsveHChXmOryoVUwOwl/zIJFJn4jG+IRWix0EZvZRUQz3FXC85joT9jnw7u1w8FPt3vsCdMfVtSColpHRx3bYZb6Ba340t+UDEigKcMipyoGJNCKCf1NyYAEmjIscqpiQAKtmNDflAxIoCnDIqcqBiTQign9TcmABJoyLHKqYuB/oj2igrDvnw8AAAAASUVORK5CYII=",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = TSToMat(cmap='spring')(TSTensor(X[:2]), split_idx=0)\n",
    "print(out.shape)\n",
    "out[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@delegates(GramianAngularField.__init__)\n",
    "class TSToGADF(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Gramian Angular Difference Field.\n",
    "    It requires either input to be previously normalized between -1 and 1 or set range to (-1, 1)\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, cmap=None, range=None, **kwargs):\n",
    "        self.size,self.cmap,self.range = size,cmap,range\n",
    "        self.encoder = GramianAngularField(image_size=1., sample_range=self.range, method='d', **kwargs)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        bs, *_, seq_len = o.shape\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        if size != seq_len:\n",
    "            o = F.interpolate(o.reshape(-1, 1, seq_len), size=size, mode='nearest', align_corners=None)[:, 0]\n",
    "        else:\n",
    "            o = o.reshape(-1, seq_len)\n",
    "        output = self.encoder.fit_transform(o.cpu().numpy()).reshape(bs, -1, size, size) / 2 + .5\n",
    "        if self.cmap and output.shape[1] == 1:\n",
    "            output = TSImage(plt.get_cmap(self.cmap)(output)[..., :3]).squeeze(1).permute(0,3,1,2)\n",
    "        else: output = TSImage(output)\n",
    "        return output.to(device=o.device)\n",
    "\n",
    "\n",
    "@delegates(GramianAngularField.__init__)\n",
    "class TSToGASF(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Gramian Angular Summation Field.\n",
    "    It requires either input to be previously normalized between -1 and 1 or set range to (-1, 1)\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, cmap=None, range=None, **kwargs):\n",
    "        self.size,self.cmap,self.range = size,cmap,range\n",
    "        self.encoder = GramianAngularField(image_size=1., sample_range=self.range, method='s', **kwargs)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        bs, *_, seq_len = o.shape\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        if size != seq_len:\n",
    "            o = F.interpolate(o.reshape(-1, 1, seq_len), size=size, mode='nearest', align_corners=None)[:, 0]\n",
    "        else:\n",
    "            o = o.reshape(-1, seq_len)\n",
    "        output = self.encoder.fit_transform(o.cpu().numpy()).reshape(bs, -1, size, size) / 2 + .5\n",
    "        if self.cmap and output.shape[1] == 1:\n",
    "            output = TSImage(plt.get_cmap(self.cmap)(output)[..., :3]).squeeze(1).permute(0,3,1,2)\n",
    "        else: output = TSImage(output)\n",
    "        return output.to(device=o.device)\n",
    "\n",
    "\n",
    "\n",
    "@delegates(MarkovTransitionField.__init__)\n",
    "class TSToMTF(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Markov Transition Field\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, cmap=None, n_bins=5, **kwargs):\n",
    "        self.size,self.cmap = size,cmap\n",
    "        self.encoder = MarkovTransitionField(n_bins=n_bins, **kwargs)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        bs, *_, seq_len = o.shape\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        if size != seq_len:\n",
    "            o = F.interpolate(o.reshape(-1, 1, seq_len), size=size, mode='nearest', align_corners=None)[:, 0]\n",
    "        else:\n",
    "            o = o.reshape(-1, seq_len)\n",
    "        output = self.encoder.fit_transform(o.cpu().numpy()).reshape(bs, -1, size, size)\n",
    "        if self.cmap and output.shape[1] == 1:\n",
    "            output = TSImage(plt.get_cmap(self.cmap)(output)[..., :3]).squeeze(1).permute(0,3,1,2)\n",
    "        else: output = TSImage(output)\n",
    "        return output.to(device=o.device)\n",
    "\n",
    "\n",
    "@delegates(RecurrencePlot.__init__)\n",
    "class TSToRP(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Recurrence Plot.\n",
    "    It requires input to be previously normalized between -1 and 1\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, cmap=None, **kwargs):\n",
    "        self.size,self.cmap = size,cmap\n",
    "        self.encoder = RecurrencePlot(**kwargs)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        bs, *_, seq_len = o.shape\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        if size != seq_len:\n",
    "            o = F.interpolate(o.reshape(-1, 1, seq_len), size=size, mode='nearest', align_corners=None)[:, 0]\n",
    "        else:\n",
    "            o = o.reshape(-1, seq_len)\n",
    "        output = self.encoder.fit_transform(o.cpu().numpy()) / 2\n",
    "        output = output.reshape(bs, -1, size, size)\n",
    "        if self.cmap and output.shape[1] == 1:\n",
    "            output = TSImage(plt.get_cmap(self.cmap)(output)[..., :3]).squeeze(1).permute(0,3,1,2)\n",
    "        else: output = TSImage(output)\n",
    "        return output.to(device=o.device)\n",
    "\n",
    "\n",
    "@delegates(JointRecurrencePlot.__init__)\n",
    "class TSToJRP(Transform):\n",
    "    r\"\"\"Transforms a time series batch to a 4d TSImage (bs, n_vars, size, size) by applying Joint Recurrence Plot\"\"\"\n",
    "    order = 98\n",
    "\n",
    "    def __init__(self, size=224, cmap=None, **kwargs):\n",
    "        self.size,self.cmap = size,cmap\n",
    "        self.encoder = JointRecurrencePlot(**kwargs)\n",
    "\n",
    "    def encodes(self, o: TSTensor):\n",
    "        o = to3d(o)\n",
    "        bs, *_, seq_len = o.shape\n",
    "        size = ifnone(self.size, seq_len)\n",
    "        if size != seq_len: o = F.interpolate(o, size=size, mode='nearest', align_corners=None)\n",
    "        output = self.encoder.fit_transform(o.cpu().numpy()).reshape(bs, -1, size, size)\n",
    "        if self.cmap and output.shape[1] == 1:\n",
    "            output = TSImage(plt.get_cmap(self.cmap)(output)[..., :3]).squeeze(1).permute(0,3,1,2)\n",
    "        else: output = TSImage(output)\n",
    "        return output.to(device=o.device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 24, 224, 224])\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = TSToRP()(TSTensor(X[:2]), split_idx=0)\n",
    "print(out.shape)\n",
    "out[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "o = TSTensor(X[0][1][None])\n",
    "encoder = RecurrencePlot()\n",
    "a = encoder.fit_transform(o.cpu().numpy())[0]\n",
    "o = TSTensor(X[0])\n",
    "encoder = RecurrencePlot()\n",
    "b = encoder.fit_transform(o.cpu().numpy())[1]\n",
    "test_eq(a,b) # channels can all be processed in parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_eq(TSToRP()(TSTensor(X[0]), split_idx=False)[0], TSToRP()(TSTensor(X[0][0][None]), split_idx=False)[0])\n",
    "test_eq(TSToRP()(TSTensor(X[0]), split_idx=False)[1], TSToRP()(TSTensor(X[0][1][None]), split_idx=False)[0])\n",
    "test_eq(TSToRP()(TSTensor(X[0]), split_idx=False)[2], TSToRP()(TSTensor(X[0][2][None]), split_idx=False)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Plot TSImage(shape:torch.Size([8, 3, 100, 100])) torch.float32 0.054901961237192154 1.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Mat TSImage(shape:torch.Size([8, 3, 100, 100])) torch.float32 0.019607843831181526 1.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "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",
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 GADF TSImage(shape:torch.Size([8, 24, 100, 100])) torch.float32 2.980232238769531e-07 0.9999997019767761\n"
     ]
    },
    {
     "data": {
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BKzaGxDYCGkFHId1O0PXlH7G92O7/GWTEUGvW9MCQ6Gf+maEsKV09JKXWwPIPJ3pzIhpJs2RYpCwgONPjUGMnm7PxtKgBD3ihSAhJ74U0AO/+i0GNfA0IYJoHwYeeJ4XDbfd/yd3E9GIXjpIAAAAASUVORK5CYII=",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 GASF TSImage(shape:torch.Size([8, 24, 100, 100])) torch.float32 0.0 0.938302218914032\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "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",
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 MTF TSImage(shape:torch.Size([8, 24, 100, 100])) torch.float32 0.0 1.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 RP TSImage(shape:torch.Size([8, 24, 100, 100])) torch.float32 0.0 0.8106333613395691\n"
     ]
    },
    {
     "data": {
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      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=100x100>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsid = 'NATOPS'\n",
    "X, y, splits = get_UCR_data(dsid, return_split=False)\n",
    "tfms = [None, Categorize()]\n",
    "bts = [[TSNormalize(), TSToPlot(100)],\n",
    "       [TSNormalize(), TSToMat(100)],\n",
    "       [TSNormalize(), TSToGADF(100)],\n",
    "       [TSNormalize(), TSToGASF(100)],\n",
    "       [TSNormalize(), TSToMTF(100)],\n",
    "       [TSNormalize(), TSToRP(100)]]\n",
    "btns = ['Plot', 'Mat', 'GADF', 'GASF', 'MTF', 'RP']\n",
    "dsets = TSDatasets(X, y, tfms=tfms, splits=splits)\n",
    "for i, (bt, btn) in enumerate(zip(bts, btns)):\n",
    "    dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=8, batch_tfms=bt)\n",
    "    test_eq(dls.vars, 3 if i <2 else X.shape[1])\n",
    "    test_eq(dls.vars, 3 if i <2 else X.shape[1])\n",
    "    test_eq(dls.len, (100,100))\n",
    "    xb, yb = dls.train.one_batch()\n",
    "    print(i, btn, xb, xb.dtype, xb.min(), xb.max())\n",
    "    xb[0].show()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest way to train a model using time series to image transforms is this: \n",
    "```\n",
    "dsid = 'NATOPS'\n",
    "X, y, splits = get_UCR_data(dsid, return_split=False)\n",
    "tfms = [None, Categorize()]\n",
    "batch_tfms = [TSNormalize(), TSToGADF(224)]\n",
    "dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "learn = tsimage_learner(dls, xresnet34)\n",
    "learn.fit_one_cycle(10)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "IPython.notebook.save_checkpoint();",
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/nacho/notebooks/tsai/nbs/012_data.image.ipynb saved at 2024-02-10 22:40:32\n",
      "Correct notebook to script conversion! 😃\n",
      "Saturday 10/02/24 22:40:35 CET\n"
     ]
    },
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       "              "
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#|eval: false\n",
    "#|hide\n",
    "from tsai.export import get_nb_name; nb_name = get_nb_name(locals())\n",
    "from tsai.imports import create_scripts; create_scripts(nb_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
